{"id":"W2506778745","doi":"10.1002/jbio.201600021","title":"In‐vivo multispectral video endoscopy towards in‐vivo hyperspectral video endoscopy","year":2016,"lang":"en","type":"article","venue":"Journal of Biophotonics","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Medizinische Fakultät, Friedrich-Alexander-Universität Erlangen-Nürnberg; Erlangen Graduate School of Advanced Optical Technologies; Deutsche Forschungsgemeinschaft; Natural Sciences and Engineering Research Council of Canada; Friedrich-Alexander-Universität Erlangen-Nürnberg","keywords":"Multispectral image; Hyperspectral imaging; Endoscopy; In vivo; Artificial intelligence; Computer science; Support vector machine; Cancer detection; Cancer; Pattern recognition (psychology); Computer vision; Pathology; Medical physics; Radiology; Medicine; Biology; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001629912,0.0006159275,0.0002750401,0.0004109349,0.0002005178,0.0009300737,0.000686022,0.0009384844,0.005301276],"category_scores_gemma":[0.0009958494,0.0003170418,0.0004067196,0.0003404123,0.0008757222,0.0009803174,0.0006271417,0.001315271,0.001190304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002360046,"about_ca_system_score_gemma":0.0003563936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006600114,"about_ca_topic_score_gemma":0.0009370035,"domain_scores_codex":[0.9992056,0.000483084,0.00003296856,0.00009753563,0.0001211266,0.00005963069],"domain_scores_gemma":[0.9990126,0.0003015769,0.0002015168,0.0001620421,0.0002117848,0.0001105688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007118649,0.0002429568,0.003825698,0.0006295103,0.00003895621,0.0002588307,0.0001083199,0.0007260813,0.9402341,0.001292263,0.002758751,0.0491726],"study_design_scores_gemma":[0.00009566537,0.002971195,0.04308826,0.0001868513,0.000144542,0.01036645,0.0002499419,0.01578446,0.8896347,0.001728386,0.03561239,0.0001371799],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4728018,0.01292411,0.482407,0.005676602,0.001092425,0.0004136897,0.0005296938,0.001371304,0.02278345],"genre_scores_gemma":[0.740514,0.008372446,0.2386756,0.002054396,0.0006015621,0.0001677667,0.000608537,0.0002264799,0.008779282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005301276,"threshold_uncertainty_score":0.01773453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01380985685489416,"score_gpt":0.2762663258714457,"score_spread":0.2624564690165516,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}